Talker-Specific Audio Enhancement for Personalized Hearing Profiles
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Solution Overview
Problem
Traditional hearing aid technologies provide inadequate personalization for individuals with hearing loss, failing to account for the unique acoustic characteristics of specific talkers or sources, and are limited by regulatory constraints that restrict professional audiologists, increasing cost and reducing availability.
Innovation Solution
A system and method for creating personalized audio processing settings using a personalization node that establishes media sessions, identifies user profiles, and applies tuning profiles based on the characteristics of specific talkers or sources, enabling dynamic adjustment of audio data processing parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional generalized hearing prescriptions are used, then device complexity is reduced and ease of manufacture is improved, but adaptability to individual users and talkers deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-establishing communication sessions between electronic devices and pre-storing tuning profiles in the personalization database before actual audio processing is needed. This allows the system to have personalized processing parameters ready in advance, enabling quick adaptation without real-time complexity.
Solution Approach 2:
The system creates copies of audio processing parameters through tuning profiles that are stored in the personalization database. These profiles contain copied and adapted processing settings that can be retrieved and applied to different talkers and media types, enabling personalization without requiring complete new parameter sets for each scenario.
2Reliability
If personalized tuning profiles for each talker are implemented, then speech intelligibility for hearing-impaired users is improved, but device complexity and data storage requirements increase
Solution Approach 1:
The system segments audio processing parameters into distinct tuning profiles organized by talker identity and media type. Each profile contains specific processing parameters tailored to particular talkers or media characteristics, allowing the system to manage complexity through organized segmentation rather than monolithic parameter sets.
Solution Approach 2:
The system establishes equipotentiality by creating a standardized framework where all tuning profiles follow the same structure and storage format in the personalization database. This uniform structure allows complex personalized data to be managed efficiently through consistent organization, reducing the perceived complexity despite the volume of personalized information.
3Measurement precision
If real-time identification and application of tuning profiles is performed, then audio processing accuracy is improved, but processing time and system response delay increase
Solution Approach 1:
The system performs preliminary identification and preparation of tuning profiles before actual audio processing begins. By establishing communication sessions and retrieving appropriate profiles in advance, the system minimizes real-time processing delays while maintaining high accuracy in parameter selection.
Solution Approach 2:
The system uses feedback mechanisms to identify talker identity and media characteristics, then selects and applies the corresponding tuning profile. This feedback loop enables accurate real-time adaptation by continuously monitoring input characteristics and adjusting processing parameters based on pre-prepared profiles.
Data Source
AI summary
System and techniques for identifying and applying personalized audio processing parameter settings for a listener with hearing loss and/or certain listening preferences. The listener creates and allows for automatic recall of a first profile representing a set of audio enhancement processing parameters associated with the listener as well as create and recall a source-dependent profile representing a further improvement, or deviation, from the first profile associated with a source signal or a category of source signals, such as the voice of a given talker.